Tercile Probabilities

Experimental Precipitation Subseasonal Forecasts

The default map shows the latest forecast issued, for combined weeks #2 and #3 lead
time (i.e., the 14-day long period 8 to 21 days after the forecast is issued), as
probability of the dominant tercile. Forecasts are started weekly (on Thursdays) but
are issued with 1 to 2 months delay. Previous forecast issues can be consulted through
the control bar menu. Lead time for combined weeks #3 and #4 (i.e., the 14-day long
period 15 to 28 days after the forecast is issued) are also available. The smaller
side map shows a verification of the forecast in current view as the observed tercile
values according to the 1999-2010 training period of the calibration of the forecast.

Clicking on the map will show, for the clicked grid box, the probabilities for the
3 forecasts categories (Below-, Near- and Above- Normal).

The probabilistic forecasts shown here are obtained from the statistical calibration
of three models from the Subseasonal to Seasonal (S2S) Prediction Project database
(Vitart et al, 2017) which are combined with equal weight to form multi-model ensemble
precipitation tercile probabilities forecasts. Individual model forecasts are calibrated
separately for each point, start and lead using Extended Logistic Regressions (ELR;
Vigaud et al, 2017) based on the historical performance of each model, and thus provide
reliable intra-seasonal climate information in regards to a wide range of climate
risk of concerns to the decision making communities and for which subseasonal forecasts
are particularly well suited.

As subseasonal-to-seasonal (S2S) forecasting techniques are being developped, and
more and more models are made available in (near) real-time, this Maproom shows the
type of forecast information that can be currently delivered at these time scales.

While the verification map gives you a sense of the performance of a specific forecast
issue, you can navigate from here to the Historical Skill Maproom to explore skill
scores of historical performance of the forecasting system, by climatological month
of issue.